Yuanchen Bai
Papers
1
Total Citations
1
H-Index
1
About
Yuanchen Bai is a researcher advancing the frontier of preference-based robot learning, with a focus on aligning autonomous systems with nuanced human values. His key research areas include human-robot interaction, preference elicitation, and interactive machine learning for robotics. Bai’s major contribution is the development of FARPLS (Feature-Augmented Robot Trajectory Preference Labeling System), a novel framework that assists human labelers in more effectively communicating their preferences to robots. Unlike traditional pairwise comparison systems that offer little support for digesting complex trajectory data, FARPLS augments the labeling process with salient features, enabling labelers to identify and articulate subtle distinctions between robot behaviors. This work directly addresses a critical bottleneck in preference-based reinforcement learning: the cognitive load and ambiguity faced by human annotators. By improving the efficiency and accuracy of preference elicitation, Bai’s research paves the way for robots that can learn safer, more personalized task executions. His contributions are particularly impactful for applications in assistive robotics and autonomous navigation, where aligning robot objectives with human intent is paramount.
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